Advanced Analytics in Tableau: A Comprehensive Guide

Most companies today rely on data-driven decision-making to drive business growth. Data-driven decision-making can be defined as the process of making organizational decisions based on actual data rather than intuition or observation alone.

• This has led to an exponential increase in the use of various Data Analytics and Business Intelligence techniques in most companies.
• There are a wide variety of tools available in the market that are leveraged by businesses to perform an in-depth analysis of their data in order to plan future Growth, Product, and Marketing strategies accordingly. One of the most well-known Business Intelligence tools in Tableau.  

What is Advanced Analytics?

Advanced Analytics is the process of autonomously or semi-autonomously analyzing data to extract valuable insights. It uses sophisticated techniques like Predictive Analytics, Recommendation Systems, and Statistical Forecasting to identify trends and behaviors. The goal is to uncover deeper insights from data, often leveraging Machine Learning and Data Science methods.

The process also includes pre-processing the data to better understand the problem. Techniques like Multivariate Visualization are used to assess feature separability. Additionally, building Classifiers or Regressors with appropriate Machine Learning methods helps evaluate the final data analysis pipeline thoroughly.

What is Tableau?

Tableau is a popular Data Analysis & Visualization tool that was built in 2003. It started out as a Computer Science project at Stanford University that aimed to improve the flow of analysis and make data more accessible to people through interactive visualization techniques. 

Key Features of Tableau

Some of the key features of Tableau are as follows:

• Predictive Analytics
• Advanced Dashboard
• In-Memory and Live Data
• Attractive Visualizations
• Robust Security

Advanced Analytics in Tableau

The following scenarios can easily be handled using Advanced Analytics in Tableau:

• Segmentation and Cohort Analytics in Tableau
• Scenarios and What-If Analytics in Tableau
• Sophisticated Calculations and Statistical Functions
• Time-Series and Predictive Analytics in Tableau
• R and Python Integration

1. Segmentation and Cohort Analytics in Tableau

Tableau provides flexible tools for intuitive and rapid Cohort Analysis & Segmentation. It can seamlessly perform slicing and dicing operations on data along as many dimensions as required. Automated Clustering can also help in this Segmentation using the pre-processed datasets. 

Features for Segmentation and Cohort Analytics in Tableau

A) Clustering

• Clustering is an Unsupervised Machine Learning technique used for segmenting data, especially when working with many variables.
• Tableau supports clustering, allowing users to group data into meaningful segments based on shared characteristics.
• Its interactive and flexible interface helps users test various hypotheses and explore distributions across different cohorts.

B) Sets & Sets Actions

• Sets are used to define collections of data objects, either manually or through automated logic.
• They are useful for tasks like filtering, highlighting, cohort calculations, and outlier analysis.
• Tableau allows combining multiple sets to test scenarios or create cohorts for simulations.
• Examples include grouping customers for retention analysis or patients for health and non-health control studies.

C) Grouping

• Grouping is a feature for creating Ad-hoc Categories and establishing hierarchies. Groups can also help with basic Data Cleaning operations.
• Groups can be useful when data is not consistent by providing consistency and quality issues.

2. Scenarios and What-If Analytics in Tableau

Tableau offers a very flexible User Interface that can be used with powerful input capabilities that are extremely helpful in making calculations and testing different scenarios. So by just using the interface and changing the parameters, its effect on the output can be seen. This feature is useful for quick Data Analysis and to validate different results. 

Features for Scenarios and What-If Analytics in Tableau

A) Parameters

• Parameters allow users to change the base values of calculations or initial conditions.
• They are useful for setting filter thresholds, performing calculations, and offering flexible dashboard options.
• This feature lets users choose the data they want to see, making dashboards more interactive and customizable.
• Parameters help non-technical users easily explore and understand the specific sections of data they are interested in.

B) Story Points

• Story Points in Tableau enable users to define scenarios and validate them using data.
• Features like Sets, Groups, Parameters, and Drag-and-Drop Segmentation aid in creating visual and understandable stories.
• These stories are designed to be easily interpreted by both technical and non-technical users within an organization.
• Tableau’s What-If Analytics supports complex data analysis with enhanced visualization, simplifying data interpretation.
• This ultimately improves decision-making by providing clear insights into data-driven scenarios.

3. Sophisticated Calculations and Statistical Functions

Tableau has many sophisticated and detailed functionalities including the implementation of Statistical Methodologies like Skewness, Correlation, Covariance, Kurtosis, Mode, Standard Deviation, etc. along with Statistical Models like Naive Bayes, K-Means, Random Forest, etc. All these techniques help in better understanding of data and making predictions seamlessly when required.

Features of Sophisticated Calculations and Statistical Functions

A) Calculated Fields

Calculated Fields in Tableau enable users to solve complex computations and create new data points from existing data.

These fields support arithmetic operations and advanced logic for in-depth analysis.

Level of Detail (LOD) Expressions:

• Crucial for performing complex data analyses, LOD expressions are part of Tableau’s calculation language.
• Introduced in Tableau 9, they allow detailed analysis that wasn’t possible in earlier versions.

Table Calculations:

• Applied to data within the visualization, these calculations depend on the table’s structure.
• Quick Table Calculations provide pre-defined calculations accessible with a single click.
• Table Calculation Functions allow customization for additional complexity, starting from a Quick Table Calculation.

Official Tableau documentation offers detailed insights into Quick Table Calculations and Table Calculation Functions for further learning.

4. Time-Series and Predictive Analytics in Tableau

Time Series is one of the most important types of analysis that can be performed on almost every data set to explore the trends and seasonality including Predictive Analysis and Forecasting. 

Features of Time-Series and Predictive Analytics in Tableau

Tableau provides a very interactive and user-friendly interface to perform Time-Series Analytics in Tableau. This analysis begins by dragging the fields of interest into the view and beginning the questionnaire process. With the help of the dual-axis feature and Discretized Aggregation, one can look at the multiple time series and perform its analysis easily.

Forecasting is another important feature of Time-Series Analytics in Tableau. By simply using the Drag-and-Drop feature, this analysis can be performed in a few clicks. 

5. R and Python Integration

R and Python integrations provide the power and ease of use of Tableau while allowing experts to leverage prior work in other platforms and handle nuanced Statistical and Machine Learning requirements.

Features of R and Python Integrations in Tableau

Tableau is a Comprehensive Analytics platform that houses the ability to integrate with other Advanced Analytics technologies, allowing you to expand the possible functionality and leverage existing investments in other solutions. For further details, you can have a look at the Official Documentation.

Conclusion

• This article gave you an in-depth understanding of the various types of Advanced Analytics in Tableau.
• These Advanced Analytics features make Tableau one of the best tools available for Data Analytics & Visualization allowing organizations to make smart decisions.
• Most modern businesses use multiple platforms to run their day-to-day operations. In order to perform any analysis on this operational data, the data would first have to be integrated from all these platforms and stored in a centralized location.
• Making an in-house Data Integration platform would require immense engineering bandwidth. Businesses can instead choose to use Automated Data Integration like Hevo.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!

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